Resetting of Dynamically Grown Accelerated Data Structure

ABSTRACT

A circuit arrangement, program product and method are provided for resetting a dynamically grown Accelerated Data Structure (ADS) used in image processing in which an ADS is initialized by reusing the root node of a prior ADS and resetting at least one node in the prior ADS to break a link between the reset node and a linked-to node in the prior ADS. By doing so, the memory allocated to the prior ADS may be reused for the new ADS, without having to clear or wipe out all of the allocated memory. In addition, in some instances, given the similarity of many image frames, often some or all of the node structure of a prior ADS may be reused for a new ADS, requiring only the contents of nodes to be cleared, instead of having to clear out all of the nodes in the prior ADS. As a result, the processing overhead associated with initializing a new ADS can be significantly reduced.

FIELD OF THE INVENTION

The invention is generally related to data processing, and in particularto graphical imaging processing and rendering.

BACKGROUND OF THE INVENTION

The process of rendering two-dimensional images from three-dimensionalscenes is commonly referred to as image processing. As the moderncomputer industry evolves image processing evolves as well. Oneparticular goal in the evolution of image processing is to maketwo-dimensional simulations or renditions of three-dimensional scenes asrealistic as possible. One limitation of rendering realistic images isthat modern monitors display images through the use of pixels.

A pixel is the smallest area of space which can be illuminated on amonitor. Most modern computer monitors will use a combination ofhundreds of thousands or millions of pixels to compose the entiredisplay or rendered scene. The individual pixels are arranged in a gridpattern and collectively cover the entire viewing area of the monitor.Each individual pixel may be illuminated to render a final picture forviewing.

One technique for rendering a real world three-dimensional scene onto atwo-dimensional monitor using pixels is called rasterization.Rasterization is the process of taking a two-dimensional imagerepresented in vector format (mathematical representations of geometricobjects within a scene) and converting the image into individual pixelsfor display on the monitor. Rasterization is effective at renderinggraphics quickly and using relatively low amounts of computationalpower; however, rasterization suffers from several drawbacks. Forexample, rasterization often suffers from a lack of realism because itis not based on the physical properties of light, rather rasterizationis based on the shape of three-dimensional geometric objects in a sceneprojected onto a two dimensional plane. Furthermore, the computationalpower required to render a scene with rasterization scales directly withan increase in the complexity of the scene to be rendered. As imageprocessing becomes more realistic, rendered scenes also become morecomplex. Therefore, rasterization suffers as image processing evolves,because rasterization scales directly with complexity.

Several alternative techniques rendering a real world three-dimensionalscene onto a two-dimensional monitor using pixels have been developedbased upon more realistic physical modeling. One such physical renderingtechnique is called ray tracing. The ray tracing technique traces thepropagation of imaginary rays, rays which behave similar to rays oflight, into a three-dimensional scene which is to be rendered onto acomputer screen. The rays originate from the eye(s) of a viewer sittingbehind the computer screen and traverse through pixels, which make upthe computer screen, towards the three-dimensional scene. Each tracedray proceeds into the scene and may intersect with objects within thescene. If a ray intersects an object within the scene, properties of theobject and several other contributing factors are used to calculate theamount of color and light, or lack thereof, the ray is exposed to. Thesecalculations are then used to determine the final color of the pixelthrough which the traced ray passed.

The process of tracing rays is carried out many times for a singlescene. For example, a single ray may be traced for each pixel in thedisplay. Once a sufficient number of rays have been traced to determinethe color of all of the pixels which make up the two-dimensional displayof the computer screen, the two dimensional synthesis of thethree-dimensional scene can be displayed on the computer screen to theviewer.

Ray tracing typically renders real world three-dimensional scenes withmore realism than rasterization. This is partially due to the fact thatray tracing simulates how light travels and behaves in a real worldenvironment, rather than simply projecting a three-dimensional shapeonto a two dimensional plane as is done with rasterization. Therefore,graphics rendered using ray tracing more accurately depict on a monitorwhat our eyes are accustomed to seeing in the real world.

Furthermore, ray tracing also handles increases in scene complexitybetter than rasterization as scenes become more complex. Ray tracingscales logarithmically with scene complexity. This is due to the factthat the same number of rays may be cast into a scene, even if the scenebecomes more complex. Therefore, ray tracing does not suffer in terms ofcomputational power requirements as scenes become more complex asrasterization does.

One major drawback of ray tracing, however, is the large number ofcalculations, and thus processing power, required to render scenes. Thisleads to problems when fast rendering is needed. For example, when animage processing system is to render graphics for animation purposessuch as in a game console. Due to the increased computationalrequirements for ray tracing it is difficult to render animation quicklyenough to seem realistic (realistic animation is approximately twenty totwenty-four frames per second).

With continued improvements in semiconductor technology in terms ofclock speed and increased use of parallelism; however, real timerendering of scenes using physical rendering techniques such as raytracing becomes a more practical alternative to rasterization. At thechip level, multiple processor cores are often disposed on the samechip, functioning in much the same manner as separate processor chips,or to some extent, as completely separate computers. In addition, evenwithin cores, parallelism is employed through the use of multipleexecution units that are specialized to handle certain types ofoperations. Hardware-based pipelining is also employed in many instancesso that certain operations that may take multiple clock cycles toperform are broken up into stages, enabling other operations to bestarted prior to completion of earlier operations. Multithreading isalso employed to enable multiple instruction streams to be processed inparallel, enabling more overall work to performed in any given clockcycle.

Despite these advances, however, the adoption of physical renderingtechniques faces a number of challenges. One such challenge relates tothe generation of the data structures that are utilized by such physicalrendering techniques.

In general, rendering processes often can be logically broken intofrontend and backend processes. The frontend process is used tobasically build primitives for a scene to be depicted in the displayedimage. A primitive is the basic geometry element used to represent anobject in a scene, and in many conventional techniques, primitives aredefined as triangles. Objects to be placed in a scene may be predefinedand loaded during the frontend process, or objects can be builton-the-fly based upon mathematical algorithms that define the shape of a3D object.

The frontend process typically places objects in a scene, determinesand/or creates the primitives for those objects, and assigns colors ortextures to each of the primitives. Once objects and primitives areplaced, no movement of those objects or primitives is typicallypermitted.

The backend process takes the primitives and the colors or texturesassigned to those primitives by the frontend process, and draws the 2Dimage, determining which primitives are visible from the desiredviewpoint, and based upon the displayed primitives, assigningappropriate colors to all of the pixels in the image. The output of thebackend process is fed to an image buffer for display on a videodisplay.

For a physical rendering backend, the output of the frontend process,the list of primitives and their assigned colors or textures, often mustbe transformed into a data structure that can be used by the physicalrendering backend. In many physical rendering techniques, such as raytracing and photon mapping, this data structure is referred to as anAccelerated Data Structure (ADS).

Given the relatively high processing requirements for physical renderingtechniques, the ADS enables fast and efficient retrieval of primitivesto assist in optimizing the performance of such techniques. An ADS maybe implemented, for example, as a spatial index, which divides athree-dimensional scene or world into smaller volumes (smaller relativeto the entire three-dimensional scene), within which geometricprimitives are placed. Thus, for example, a ray tracing image processingsystem can use the known boundaries of these smaller volumes todetermine if a ray may intersect primitives contained within the smallervolumes. If a ray does intersect a volume containing primitives, then aray intersection test can be run using the trajectory of the ray againstthe known location and dimensions of the primitives contained withinthat volume. If a ray does not intersect a particular volume then thereis no need to run any ray-primitive intersection tests against theprimitives contained within that volume. Furthermore, if a rayintersects a bounding volume that does not contain primitives then thereis no need to run any ray-primitive intersections tests against thatbounding volume. Thus, by reducing the number of ray-primitiveintersection tests that may be necessary, the use of a spatial indexgreatly increases the performance of a ray tracing image processingsystem.

An ADS may be implemented, for example, with a tree data structure,where nodes in the tree represent volumes within the three-dimensionalscene, and with child nodes representing sub-volumes of the volumesrepresented by their respective parent nodes. Each primitive in a sceneis typically placed in the lowest level node representing the volumewithin which such primitive is contained. By navigating from a root orworld node for the tree, and then successively down through the treebased upon the current position of a ray, and the known boundaries ofthe volumes represented by the nodes in the tree, the primitive(s) withwhich the ray could potentially intersect can be readily and efficientlyascertained.

An ADS, however, can be computationally expensive to create, and canrequire a sizable memory allocation. Moreover, given that a new ADS istypically required for each frame, the processing overhead required tocreate the ADS can be substantial.

One step that can have a significant impact on the processing overheadassociated with creating an ADS is the initialization of the ADS.Typically, the memory required for an ADS must be allocated to the ADS,either through a fixed allocation, which is limited in terms offlexibility since the size of an ADS can vary from frame to frame, orthrough dynamic allocation, so that memory is allocated as it is neededduring the creation of the ADS. In order to ensure that allocated memoryis not orphaned after an ADS is no longer being used, it may benecessary to deallocate the memory allocated to an ADS for a prior imageframe before allocating new memory for the ADS for a new image frame.Allocating and deallocating memory, however, are often computationallyexpensive operations, which can adversely impact system performance,particularly when a new ADS is created for each image frame.

In the alternative, rather than deallocating the memory allocated to theADS used for the prior image frame and allocating new memory for the ADSfor the new image frame, the same allocated memory can be reused foreach image frame. Doing so, however, would typically require all of theprevious data for the ADS to be overwritten to in effect wipe out theprior ADS from the allocated memory. Given the size of the memoryallocation for an ADS, particularly in complex scenes, the overheadassociated with clearing out the allocated memory prior to creating anew ADS would also adversely impact system performance.

Therefore, a need exists in the art for minimizing the processingoverhead associated with generating an ADS, and in particular, forminimizing the processing overhead associated with resetting orinitializing the memory in which an ADS will be created.

SUMMARY OF THE INVENTION

The invention addresses these and other problems associated with theprior art by providing a circuit arrangement, program product and methodfor resetting a dynamically grown Accelerated Data Structure (ADS) usedin image processing in which an ADS is initialized by reusing the rootnode of a prior ADS and resetting at least one node in the prior ADS tobreak a link between the reset node and a linked-to node in the priorADS. By doing so, the memory allocated to the prior ADS may be reusedfor the new ADS, without having to clear or wipe out all of theallocated memory. In addition, in some instances, given the similarityof many image frames, often some or all of the node structure of a priorADS may be reused for a new ADS, requiring only the contents of nodes tobe cleared, instead of having to clear out all of the nodes in the priorADS. As a result, the processing overhead associated with initializing anew ADS can be significantly reduced.

Therefore, consistent with one aspect of the invention, accelerated datastructures for use in image processing are built by generating a firstaccelerated data structure for a first image frame, where the firstaccelerated data structure includes a plurality of linked nodes forwhich memory is allocated in a working memory, and where the pluralityof linked nodes including a root node, and subsequently generating asecond accelerated data structure for a second image frame. Generatingthe second accelerated data structure includes initializing the secondaccelerated data structure by reusing the root node for the firstaccelerated data structure as a root node for the second accelerateddata structure and resetting at least one node in the first accelerateddata structure to break a link between the reset node and a linked-tonode among the plurality of nodes, and dynamically adding a plurality ofprimitives from a scene to the second accelerated data structure for thesecond image frame, including dynamically expanding the reset node byreestablishing the link between the reset node and the linked-to nodeand clearing primitive data from the first accelerated data structurethat is stored in the linked-to node.

These and other advantages and features, which characterize theinvention, are set forth in the claims annexed hereto and forming afurther part hereof. However, for a better understanding of theinvention, and of the advantages and objectives attained through itsuse, reference should be made to the Drawings, and to the accompanyingdescriptive matter, in which there is described exemplary embodiments ofthe invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of exemplary automated computing machineryincluding an exemplary computer useful in data processing consistentwith embodiments of the present invention.

FIG. 2 is a block diagram of an exemplary NOC implemented in thecomputer of FIG. 1.

FIG. 3 is a block diagram illustrating in greater detail an exemplaryimplementation of a node from the NOC of FIG. 2.

FIG. 4 is a block diagram illustrating an exemplary implementation of anIP block from the NOC of FIG. 2.

FIG. 5 is a block diagram of a thread pipelined software engine suitablefor implementation in the NOC of FIG. 2.

FIG. 6 is a block diagram of an exemplary software pipeline suitable forimplementation in the thread pipelined software engine of FIG. 5.

FIG. 7 is a block diagram of an exemplary hybrid rendering softwarepipeline suitable for implementation in the thread pipelined softwareengine of FIG. 5.

FIG. 8 is a diagram of an exemplary scene for illustrating the dynamicgeneration of a geometry internal representation using the GIR generatorof FIG. 7.

FIG. 9 is a block diagram of a geometry internal representationgenerated for the exemplary scene of FIG. 9.

FIG. 10 is a flowchart illustrating the program flow of a geometryplacement routine executed by the GIR generator of FIG. 7.

FIG. 11 is a flowchart illustrating the program flow of an add geometryroutine executed by the GIR generator of FIG. 7.

FIG. 12 is a block diagram of an exemplary implementation of thestreaming geometry frontend referenced in FIG. 7.

FIG. 13 is a block diagram of an exemplary implementation of the raytracing backend referenced in FIG. 7.

FIGS. 14A and 14B collectively illustrate in greater detail animplementation of the hybrid rendering software pipeline of FIG. 7.

FIG. 15 is a flowchart illustrating the program flow of a reset GIRroutine executed by an alternate implementation of the GIR generator ofFIG. 7, and suitable for resetting a dynamically grown accelerated datastructure in a manner consistent with the invention.

FIG. 16 is a flowchart illustrating the program flow of an insertprimitive routine executed during the creation of a new accelerated datastructure after execution of reset GIR routine of FIG. 15.

FIGS. 17A and 17B are block diagrams respectively illustrating a firstGIR and the storage of the first GIR in a memory.

FIGS. 18A and 18B are block diagrams respectively illustrating a secondGIR and the memory of FIG. 17B after resetting the first GIR using thereset GIR routine of FIG. 15.

FIGS. 19A and 19B are block diagrams respectively illustrating thesecond GIR of FIG. 18A and the memory of FIG. 18B after inserting afirst primitive into the second GIR using the insert primitive routineof FIG. 16.

FIGS. 20A and 20B are block diagrams respectively illustrating thesecond GIR of FIG. 19A and the memory of FIG. 19B after insertingadditional primitives into the second GIR using the insert primitiveroutine of FIG. 16.

DETAILED DESCRIPTION

Embodiments consistent with the invention utilize a dynamic accelerateddata structure (ADS) generator that implements a low-overhead reset ofan ADS to reduce the overhead associated with initializing or resettingan ADS used in image processing. The dynamic ADS generator may reset anADS by reusing the root node of a prior ADS and resetting at least onenode in the prior ADS to break a link between the reset node and alinked-to node in the prior ADS. A linked-to node in this context is anode that is the target of a link in a parent node in an ADS, and ingeneral, breaking the link to a linked-to node is typically implementedby either removing the link to the linked-to node in the linking node orotherwise reconfiguring the linking node to disable the link to thelinked-to node. In the illustrated embodiment, for example, the link toa linked-to node is broken by reconfiguring the linking node from a rootor interior node to a leaf node.

As will become more apparent below, the reset node may be the root node,whereby after the reset only the root node remains from the prior ADS.In other embodiments, however, nodes other than the root node may bereset in connection with the reset of an ADS such that at least aportion of the node structure of the prior ADS is retained after thereset, which can reduce the amount of the ADS that may need to berecreated for a new image frame, particularly in instances where theimage to be displayed in a new image frame does not vary substantiallyfrom that for the prior image frame.

Once the ADS is reset, primitives from a scene may then be added to theADS, with the reset node expanded by reestablishing the links betweenthe reset node and any previously linked-to nodes and clearing primitivedata from the first accelerated data structure that was stored in thelinked-to node. In some embodiments, the primitive data is clearedmerely by setting an empty indicator for the linked-to node, although inother embodiments clearing the data may include overwriting or wipingout the primitive data stored in the node.

In many embodiments, the dynamic ADS generator may utilize an algorithmthat effectively parallelizes the generation of the ADS, such that aplurality of parallel threads of execution may be used to generate anADS. In some embodiments, the dynamic ADS generator also enables thephysical rendering backend to begin using the ADS prior to completion ofthe ADS by the dynamic ADS generator. By doing so, both the frontend andbackend rendering processes, as well as the ADS generation process, areamendable to parallelization, enabling real time rendering usingphysical rendering techniques such as ray tracing and photon mapping.Furthermore, conventional streaming geometry frontends such as OpenGLand DirectX compatible frontends can readily be adapted for use withphysical rendering backends, thereby enabling developers to continue todevelop with known API's, as well as adapt existing software originallywritten for raster-based rendering for use in connection with physicalrendering techniques.

A streaming geometry frontend consistent with the invention may includeany geometry-based rendering frontend that is capable of generating anoutput stream of geometry primitives. Examples of suitable frontendsinclude raster-based frontends such as OpenGL and DirectX compatiblefrontends; however, other frontends, whether or not typically associatedwith raster-based rendering techniques, may be used in the alternative.A physical rendering backend consistent with the invention may includeany rendering backend that is based in whole or in part on physicalmodeling techniques. Examples include ray tracing or photon mappingbackends; however, other physical rendering backends may be used in thealternative.

A dynamic accelerated data structure (ADS) generator consistent with theinvention may include any ADS generating code capable of dynamicallygenerating a data structure for storing primitives in a manner tofacilitates fast and efficient determination of the locations ofprimitives within a scene, e.g., to determine if a ray in a ray tracingalgorithm intersects any primitives within a three dimensional space.The embodiments illustrated hereinafter utilize a branch tree ADSimplementation, although other dynamically-built data structures mayalso be used in the alternative. An ADS generator consistent with theinvention may be integrated into a frontend or backend, or may beimplemented separately from both, and simply used as an interfacebetween the frontend and backend.

In addition, as will become more apparent below, a hybrid renderingarchitecture incorporating reset functionality consistent with theinvention may be implemented in a software pipeline embodied in aNetwork On Chip (NOC) hardware architecture that provides a flexible andhighly parallel processing architecture suitable for parallelizing thefrontend, backend and ADS generation operations performed in the hybridrendering architecture. It will be appreciated, however, the inventionmay be implemented without a software pipeline and/or using otherhardware architectures.

Other variations and modifications will be apparent to one of ordinaryskill in the art. Therefore, the invention is not limited to thespecific implementations discussed herein.

Hardware and Software Environment

Now turning to the drawings, wherein like numbers denote like partsthroughout the several views, FIG. 1 illustrates exemplary automatedcomputing machinery including an exemplary computer 10 useful in dataprocessing consistent with embodiments of the present invention.Computer 10 of FIG. 1 includes at least one computer processor 12 or‘CPU’ as well as random access memory 14 (‘RAM’), which is connectedthrough a high speed memory bus 16 and bus adapter 18 to processor 12and to other components of the computer 10.

Stored in RAM 14 is an application program 20, a module of user-levelcomputer program instructions for carrying out particular dataprocessing tasks such as, for example, word processing, spreadsheets,database operations, video gaming, stock market simulations, atomicquantum process simulations, or other user-level applications. Alsostored in RAM 14 is an operating system 22. Operating systems useful inconnection with embodiments of the invention include UNIX™, Linux™,Microsoft Windows XP™, AIX™, IBM's i5/OS™, and others as will occur tothose of skill in the art. Operating system 22 and application 20 in theexample of FIG. 1 are shown in RAM 14, but many components of suchsoftware typically are stored in non-volatile memory also, e.g., on adisk drive 24.

As will become more apparent below, embodiments consistent with theinvention may be implemented within Network On Chip (NOC) integratedcircuit devices, or chips, and as such, computer 10 is illustratedincluding two exemplary NOCs: a video adapter 26 and a coprocessor 28.NOC video adapter 26, which may alternatively be referred to as agraphics adapter, is an example of an I/O adapter specially designed forgraphic output to a display device 30 such as a display screen orcomputer monitor. NOC video adapter 26 is connected to processor 12through a high speed video bus 32, bus adapter 18, and the front sidebus 34, which is also a high speed bus. NOC Coprocessor 28 is connectedto processor 12 through bus adapter 18, and front side buses 34 and 36,which is also a high speed bus. The NOC coprocessor of FIG. 1 may beoptimized, for example, to accelerate particular data processing tasksat the behest of the main processor 12.

The exemplary NOC video adapter 26 and NOC coprocessor 28 of FIG. 1 eachinclude a NOC, including integrated processor (‘IP’) blocks, routers,memory communications controllers, and network interface controllers,the details of which will be discussed in greater detail below inconnection with FIGS. 2-3. The NOC video adapter and NOC coprocessor areeach optimized for programs that use parallel processing and alsorequire fast random access to shared memory. It will be appreciated byone of ordinary skill in the art having the benefit of the instantdisclosure, however, that the invention may be implemented in devicesand device architectures other than NOC devices and devicearchitectures. The invention is therefore not limited to implementationwithin an NOC device.

Computer 10 of FIG. 1 includes disk drive adapter 38 coupled through anexpansion bus 40 and bus adapter 18 to processor 12 and other componentsof the computer 10. Disk drive adapter 38 connects non-volatile datastorage to the computer 10 in the form of disk drive 24, and may beimplemented, for example, using Integrated Drive Electronics (‘IDE’)adapters, Small Computer System Interface (‘SCSI’) adapters, and othersas will occur to those of skill in the art. Non-volatile computer memoryalso may be implemented for as an optical disk drive, electricallyerasable programmable read-only memory (so-called ‘EEPROM’ or ‘Flash’memory), RAM drives, and so on, as will occur to those of skill in theart.

Computer 10 also includes one or more input/output (‘I/O’) adapters 42,which implement user-oriented input/output through, for example,software drivers and computer hardware for controlling output to displaydevices such as computer display screens, as well as user input fromuser input devices 44 such as keyboards and mice. In addition, computer10 includes a communications adapter 46 for data communications withother computers 48 and for data communications with a datacommunications network 50. Such data communications may be carried outserially through RS-232 connections, through external buses such as aUniversal Serial Bus (‘USB’), through data communications datacommunications networks such as IP data communications networks, and inother ways as will occur to those of skill in the art. Communicationsadapters implement the hardware level of data communications throughwhich one computer sends data communications to another computer,directly or through a data communications network. Examples ofcommunications adapters suitable for use in computer 10 include modemsfor wired dial-up communications, Ethernet (IEEE 802.3) adapters forwired data communications network communications, and 802.11 adaptersfor wireless data communications network communications.

For further explanation, FIG. 2 sets forth a functional block diagram ofan example NOC 102 according to embodiments of the present invention.The NOC in FIG. 2 is implemented on a ‘chip’ 100, that is, on anintegrated circuit. NOC 102 includes integrated processor (‘IP’) blocks104, routers 110, memory communications controllers 106, and networkinterface controllers 108 grouped into interconnected nodes. Each IPblock 104 is adapted to a router 110 through a memory communicationscontroller 106 and a network interface controller 108. Each memorycommunications controller controls communications between an IP blockand memory, and each network interface controller 108 controls inter-IPblock communications through routers 110.

In NOC 102, each IP block represents a reusable unit of synchronous orasynchronous logic design used as a building block for data processingwithin the NOC. The term ‘IP block’ is sometimes expanded as‘intellectual property block,’ effectively designating an IP block as adesign that is owned by a party, that is the intellectual property of aparty, to be licensed to other users or designers of semiconductorcircuits. In the scope of the present invention, however, there is norequirement that IP blocks be subject to any particular ownership, sothe term is always expanded in this specification as ‘integratedprocessor block.’ IP blocks, as specified here, are reusable units oflogic, cell, or chip layout design that may or may not be the subject ofintellectual property. IP blocks are logic cores that can be formed asASIC chip designs or FPGA logic designs.

One way to describe IP blocks by analogy is that IP blocks are for NOCdesign what a library is for computer programming or a discreteintegrated circuit component is for printed circuit board design. InNOCs consistent with embodiments of the present invention, IP blocks maybe implemented as generic gate netlists, as complete special purpose orgeneral purpose microprocessors, or in other ways as may occur to thoseof skill in the art. A netlist is a Boolean-algebra representation(gates, standard cells) of an IP block's logical-function, analogous toan assembly-code listing for a high-level program application. NOCs alsomay be implemented, for example, in synthesizable form, described in ahardware description language such as Verilog or VHDL. In addition tonetlist and synthesizable implementation, NOCs also may be delivered inlower-level, physical descriptions. Analog IP block elements such asSERDES, PLL, DAC, ADC, and so on, may be distributed in atransistor-layout format such as GDSII. Digital elements of IP blocksare sometimes offered in layout format as well. It will also beappreciated that IP blocks, as well as other logic circuitry implementedconsistent with the invention may be distributed in the form of computerdata files, e.g., logic definition program code, that define at variouslevels of detail the functionality and/or layout of the circuitarrangements implementing such logic. Thus, while the invention has andhereinafter will be described in the context of circuit arrangementsimplemented in fully functioning integrated circuit devices and dataprocessing systems utilizing such devices, those of ordinary skill inthe art having the benefit of the instant disclosure will appreciatethat circuit arrangements consistent with the invention are capable ofbeing distributed as program products in a variety of forms, and thatthe invention applies equally regardless of the particular type ofcomputer readable or signal bearing media being used to actually carryout the distribution. Examples of computer readable or signal bearingmedia include, but are not limited to, physical, recordable type mediasuch as volatile and non-volatile memory devices, floppy disks, harddisk drives, CD-ROMs, and DVDs (among others), and transmission typemedia such as digital and analog communication links.

Each IP block 104 in the example of FIG. 2 is adapted to a router 110through a memory communications controller 106. Each memorycommunication controller is an aggregation of synchronous andasynchronous logic circuitry adapted to provide data communicationsbetween an IP block and memory. Examples of such communications betweenIP blocks and memory include memory load instructions and memory storeinstructions. The memory communications controllers 106 are described inmore detail below with reference to FIG. 3. Each IP block 104 is alsoadapted to a router 110 through a network interface controller 108,which controls communications through routers 110 between IP blocks 104.Examples of communications between IP blocks include messages carryingdata and instructions for processing the data among IP blocks inparallel applications and in pipelined applications. The networkinterface controllers 108 are also described in more detail below withreference to FIG. 3.

Routers 110, and the corresponding links 118 therebetween, implement thenetwork operations of the NOC. The links 118 may be packet structuresimplemented on physical, parallel wire buses connecting all the routers.That is, each link may be implemented on a wire bus wide enough toaccommodate simultaneously an entire data switching packet, includingall header information and payload data. If a packet structure includes64 bytes, for example, including an eight byte header and 56 bytes ofpayload data, then the wire bus subtending each link is 64 bytes wide,512 wires. In addition, each link may be bidirectional, so that if thelink packet structure includes 64 bytes, the wire bus actually contains1024 wires between each router and each of its neighbors in the network.In such an implementation, a message could include more than one packet,but each packet would fit precisely onto the width of the wire bus. Inthe alternative, a link may be implemented on a wire bus that is onlywide enough to accommodate a portion of a packet, such that a packetwould be broken up into multiple beats, e.g., so that if a link isimplemented as 16 bytes in width, or 128 wires, a 64 byte packet couldbe broken into four beats. It will be appreciated that differentimplementations may used different bus widths based on practicalphysical limits as well as desired performance characteristics. If theconnection between the router and each section of wire bus is referredto as a port, then each router includes five ports, one for each of fourdirections of data transmission on the network and a fifth port foradapting the router to a particular IP block through a memorycommunications controller and a network interface controller.

Each memory communications controller 106 controls communicationsbetween an IP block and memory. Memory can include off-chip main RAM112, memory 114 connected directly to an IP block through a memorycommunications controller 106, on-chip memory enabled as an IP block116, and on-chip caches. In NOC 102, either of the on-chip memories 114,116, for example, may be implemented as on-chip cache memory. All theseforms of memory can be disposed in the same address space, physicaladdresses or virtual addresses, true even for the memory attacheddirectly to an IP block. Memory addressed messages therefore can beentirely bidirectional with respect to IP blocks, because such memorycan be addressed directly from any IP block anywhere on the network.Memory 116 on an IP block can be addressed from that IP block or fromany other IP block in the NOC. Memory 114 attached directly to a memorycommunication controller can be addressed by the IP block that isadapted to the network by that memory communication controller—and canalso be addressed from any other IP block anywhere in the NOC.

NOC 102 includes two memory management units (‘MMUs’) 120, 122,illustrating two alternative memory architectures for NOCs consistentwith embodiments of the present invention. MMU 120 is implemented withinan IP block, allowing a processor within the IP block to operate invirtual memory while allowing the entire remaining architecture of theNOC to operate in a physical memory address space. MMU 122 isimplemented off-chip, connected to the NOC through a data communicationsport 124. The port 124 includes the pins and other interconnectionsrequired to conduct signals between the NOC and the MMU, as well assufficient intelligence to convert message packets from the NOC packetformat to the bus format required by the external MMU 122. The externallocation of the MMU means that all processors in all IP blocks of theNOC can operate in virtual memory address space, with all conversions tophysical addresses of the off-chip memory handled by the off-chip MMU122.

In addition to the two memory architectures illustrated by use of theMMUs 120, 122, data communications port 126 illustrates a third memoryarchitecture useful in NOCs capable of being utilized in embodiments ofthe present invention. Port 126 provides a direct connection between anIP block 104 of the NOC 102 and off-chip memory 112. With no MMU in theprocessing path, this architecture provides utilization of a physicaladdress space by all the IP blocks of the NOC. In sharing the addressspace bi-directionally, all the IP blocks of the NOC can access memoryin the address space by memory-addressed messages, including loads andstores, directed through the IP block connected directly to the port126. The port 126 includes the pins and other interconnections requiredto conduct signals between the NOC and the off-chip memory 112, as wellas sufficient intelligence to convert message packets from the NOCpacket format to the bus format required by the off-chip memory 112.

In the example of FIG. 2, one of the IP blocks is designated a hostinterface processor 128. A host interface processor 128 provides aninterface between the NOC and a host computer 10 in which the NOC may beinstalled and also provides data processing services to the other IPblocks on the NOC, including, for example, receiving and dispatchingamong the IP blocks of the NOC data processing requests from the hostcomputer. A NOC may, for example, implement a video graphics adapter 26or a coprocessor 28 on a larger computer 10 as described above withreference to FIG. 1. In the example of FIG. 2, the host interfaceprocessor 128 is connected to the larger host computer through a datacommunications port 130. The port 130 includes the pins and otherinterconnections required to conduct signals between the NOC and thehost computer, as well as sufficient intelligence to convert messagepackets from the NOC to the bus format required by the host computer 10.In the example of the NOC coprocessor in the computer of FIG. 1, such aport would provide data communications format translation between thelink structure of the NOC coprocessor 28 and the protocol required forthe front side bus 36 between the NOC coprocessor 28 and the bus adapter18.

FIG. 3 next illustrates a functional block diagram illustrating ingreater detail the components implemented within an IP block 104, memorycommunications controller 106, network interface controller 108 androuter 110 in NOC 102, collectively illustrated at 132. IP block 104includes a computer processor 134 and I/O functionality 136. In thisexample, computer memory is represented by a segment of random accessmemory (‘RAM’) 138 in IP block 104. The memory, as described above withreference to FIG. 2, can occupy segments of a physical address spacewhose contents on each IP block are addressable and accessible from anyIP block in the NOC. The processors 134, I/O capabilities 136, andmemory 138 in each IP block effectively implement the IP blocks asgenerally programmable microcomputers. As explained above, however, inthe scope of the present invention, IP blocks generally representreusable units of synchronous or asynchronous logic used as buildingblocks for data processing within a NOC. Implementing IP blocks asgenerally programmable microcomputers, therefore, although a commonembodiment useful for purposes of explanation, is not a limitation ofthe present invention.

In NOC 102 of FIG. 3, each memory communications controller 106 includesa plurality of memory communications execution engines 140. Each memorycommunications execution engine 140 is enabled to execute memorycommunications instructions from an IP block 104, includingbidirectional memory communications instruction flow 141, 142, 144between the network and the IP block 104. The memory communicationsinstructions executed by the memory communications controller mayoriginate, not only from the IP block adapted to a router through aparticular memory communications controller, but also from any IP block104 anywhere in NOC 102. That is, any IP block in the NOC can generate amemory communications instruction and transmit that memorycommunications instruction through the routers of the NOC to anothermemory communications controller associated with another IP block forexecution of that memory communications instruction. Such memorycommunications instructions can include, for example, translationlookaside buffer control instructions, cache control instructions,barrier instructions, and memory load and store instructions.

Each memory communications execution engine 140 is enabled to execute acomplete memory communications instruction separately and in parallelwith other memory communications execution engines. The memorycommunications execution engines implement a scalable memory transactionprocessor optimized for concurrent throughput of memory communicationsinstructions. Memory communications controller 106 supports multiplememory communications execution engines 140 all of which runconcurrently for simultaneous execution of multiple memorycommunications instructions. A new memory communications instruction isallocated by the memory communications controller 106 to a memorycommunications engine 140 and memory communications execution engines140 can accept multiple response events simultaneously. In this example,all of the memory communications execution engines 140 are identical.Scaling the number of memory communications instructions that can behandled simultaneously by a memory communications controller 106,therefore, is implemented by scaling the number of memory communicationsexecution engines 140.

In NOC 102 of FIG. 3, each network interface controller 108 is enabledto convert communications instructions from command format to networkpacket format for transmission among the IP blocks 104 through routers110. The communications instructions may be formulated in command formatby the IP block 104 or by memory communications controller 106 andprovided to the network interface controller 108 in command format. Thecommand format may be a native format that conforms to architecturalregister files of IP block 104 and memory communications controller 106.The network packet format is typically the format required fortransmission through routers 110 of the network. Each such message iscomposed of one or more network packets. Examples of such communicationsinstructions that are converted from command format to packet format inthe network interface controller include memory load instructions andmemory store instructions between IP blocks and memory. Suchcommunications instructions may also include communications instructionsthat send messages among IP blocks carrying data and instructions forprocessing the data among IP blocks in parallel applications and inpipelined applications.

In NOC 102 of FIG. 3, each IP block is enabled to sendmemory-address-based communications to and from memory through the IPblock's memory communications controller and then also through itsnetwork interface controller to the network. A memory-address-basedcommunications is a memory access instruction, such as a loadinstruction or a store instruction, that is executed by a memorycommunication execution engine of a memory communications controller ofan IP block. Such memory-address-based communications typicallyoriginate in an IP block, formulated in command format, and handed offto a memory communications controller for execution.

Many memory-address-based communications are executed with messagetraffic, because any memory to be accessed may be located anywhere inthe physical memory address space, on-chip or off-chip, directlyattached to any memory communications controller in the NOC, orultimately accessed through any IP block of the NOC—regardless of whichIP block originated any particular memory-address-based communication.Thus, in NOC 102, all memory-address-based communications that areexecuted with message traffic are passed from the memory communicationscontroller to an associated network interface controller for conversionfrom command format to packet format and transmission through thenetwork in a message. In converting to packet format, the networkinterface controller also identifies a network address for the packet independence upon the memory address or addresses to be accessed by amemory-address-based communication. Memory address based messages areaddressed with memory addresses. Each memory address is mapped by thenetwork interface controllers to a network address, typically thenetwork location of a memory communications controller responsible forsome range of physical memory addresses. The network location of amemory communication controller 106 is naturally also the networklocation of that memory communication controller's associated router110, network interface controller 108, and IP block 104. The instructionconversion logic 150 within each network interface controller is capableof converting memory addresses to network addresses for purposes oftransmitting memory-address-based communications through routers of aNOC.

Upon receiving message traffic from routers 110 of the network, eachnetwork interface controller 108 inspects each packet for memoryinstructions. Each packet containing a memory instruction is handed tothe memory communications controller 106 associated with the receivingnetwork interface controller, which executes the memory instructionbefore sending the remaining payload of the packet to the IP block forfurther processing. In this way, memory contents are always prepared tosupport data processing by an IP block before the IP block beginsexecution of instructions from a message that depend upon particularmemory content.

In NOC 102 of FIG. 3, each IP block 104 is enabled to bypass its memorycommunications controller 106 and send inter-IP block, network-addressedcommunications 146 directly to the network through the IP block'snetwork interface controller 108. Network-addressed communications aremessages directed by a network address to another IP block. Suchmessages transmit working data in pipelined applications, multiple datafor single program processing among IP blocks in a SIMD application, andso on, as will occur to those of skill in the art. Such messages aredistinct from memory-address-based communications in that they arenetwork addressed from the start, by the originating IP block whichknows the network address to which the message is to be directed throughrouters of the NOC. Such network-addressed communications are passed bythe IP block through I/O functions 136 directly to the IP block'snetwork interface controller in command format, then converted to packetformat by the network interface controller and transmitted throughrouters of the NOC to another IP block. Such network-addressedcommunications 146 are bi-directional, potentially proceeding to andfrom each IP block of the NOC, depending on their use in any particularapplication. Each network interface controller, however, is enabled toboth send and receive such communications to and from an associatedrouter, and each network interface controller is enabled to both sendand receive such communications directly to and from an associated IPblock, bypassing an associated memory communications controller 106.

Each network interface controller 108 in the example of FIG. 3 is alsoenabled to implement virtual channels on the network, characterizingnetwork packets by type. Each network interface controller 108 includesvirtual channel implementation logic 148 that classifies eachcommunication instruction by type and records the type of instruction ina field of the network packet format before handing off the instructionin packet form to a router 110 for transmission on the NOC. Examples ofcommunication instruction types include inter-IP blocknetwork-address-based messages, request messages, responses to requestmessages, invalidate messages directed to caches; memory load and storemessages; and responses to memory load messages, etc.

Each router 110 in the example of FIG. 3 includes routing logic 152,virtual channel control logic 154, and virtual channel buffers 156. Therouting logic typically is implemented as a network of synchronous andasynchronous logic that implements a data communications protocol stackfor data communication in the network formed by the routers 110, links118, and bus wires among the routers. Routing logic 152 includes thefunctionality that readers of skill in the art might associate inoff-chip networks with routing tables, routing tables in at least someembodiments being considered too slow and cumbersome for use in a NOC.Routing logic implemented as a network of synchronous and asynchronouslogic can be configured to make routing decisions as fast as a singleclock cycle. The routing logic in this example routes packets byselecting a port for forwarding each packet received in a router. Eachpacket contains a network address to which the packet is to be routed.

In describing memory-address-based communications above, each memoryaddress was described as mapped by network interface controllers to anetwork address, a network location of a memory communicationscontroller. The network location of a memory communication controller106 is naturally also the network location of that memory communicationcontroller's associated router 110, network interface controller 108,and IP block 104. In inter-IP block, or network-address-basedcommunications, therefore, it is also typical for application-level dataprocessing to view network addresses as the location of an IP blockwithin the network formed by the routers, links, and bus wires of theNOC. FIG. 2 illustrates that one organization of such a network is amesh of rows and columns in which each network address can beimplemented, for example, as either a unique identifier for each set ofassociated router, IP block, memory communications controller, andnetwork interface controller of the mesh or x, y coordinates of eachsuch set in the mesh.

In NOC 102 of FIG. 3, each router 110 implements two or more virtualcommunications channels, where each virtual communications channel ischaracterized by a communication type. Communication instruction types,and therefore virtual channel types, include those mentioned above:inter-IP block network-address-based messages, request messages,responses to request messages, invalidate messages directed to caches;memory load and store messages; and responses to memory load messages,and so on. In support of virtual channels, each router 110 in theexample of FIG. 3 also includes virtual channel control logic 154 andvirtual channel buffers 156. The virtual channel control logic 154examines each received packet for its assigned communications type andplaces each packet in an outgoing virtual channel buffer for thatcommunications type for transmission through a port to a neighboringrouter on the NOC.

Each virtual channel buffer 156 has finite storage space. When manypackets are received in a short period of time, a virtual channel buffercan fill up—so that no more packets can be put in the buffer. In otherprotocols, packets arriving on a virtual channel whose buffer is fullwould be dropped. Each virtual channel buffer 156 in this example,however, is enabled with control signals of the bus wires to advisesurrounding routers through the virtual channel control logic to suspendtransmission in a virtual channel, that is, suspend transmission ofpackets of a particular communications type. When one virtual channel isso suspended, all other virtual channels are unaffected—and can continueto operate at full capacity. The control signals are wired all the wayback through each router to each router's associated network interfacecontroller 108. Each network interface controller is configured to, uponreceipt of such a signal, refuse to accept, from its associated memorycommunications controller 106 or from its associated IP block 104,communications instructions for the suspended virtual channel. In thisway, suspension of a virtual channel affects all the hardware thatimplements the virtual channel, all the way back up to the originatingIP blocks.

One effect of suspending packet transmissions in a virtual channel isthat no packets are ever dropped. When a router encounters a situationin which a packet might be dropped in some unreliable protocol such as,for example, the Internet Protocol, the routers in the example of FIG. 3may suspend by their virtual channel buffers 156 and their virtualchannel control logic 154 all transmissions of packets in a virtualchannel until buffer space is again available, eliminating any need todrop packets. The NOC of FIG. 3, therefore, may implement highlyreliable network communications protocols with an extremely thin layerof hardware.

The example NOC of FIG. 3 may also be configured to maintain cachecoherency between both on-chip and off-chip memory caches. Each NOC cansupport multiple caches each of which operates against the sameunderlying memory address space. For example, caches may be controlledby IP blocks, by memory communications controllers, or by cachecontrollers external to the NOC. Either of the on-chip memories 114, 116in the example of FIG. 2 may also be implemented as an on-chip cache,and, within the scope of the present invention, cache memory can beimplemented off-chip also.

Each router 110 illustrated in FIG. 3 includes five ports, four ports158A-D connected through bus wires 118 to other routers and a fifth port160 connecting each router to its associated IP block 104 through anetwork interface controller 108 and a memory communications controller106. As can be seen from the illustrations in FIGS. 2 and 3, the routers110 and the links 118 of the NOC 102 form a mesh network with verticaland horizontal links connecting vertical and horizontal ports in eachrouter. In the illustration of FIG. 3, for example, ports 158A, 158C and160 are termed vertical ports, and ports 158B and 158D are termedhorizontal ports.

FIG. 4 next illustrates in another manner one exemplary implementationof an IP block 104 consistent with the invention, implemented as aprocessing element partitioned into an instruction unit (IU) 162,execution unit (XU) 164 and auxiliary execution unit (AXU) 166. In theillustrated implementation, IU 162 includes a plurality of instructionbuffers 168 that receive instructions from an L1 instruction cache(iCACHE) 170. Each instruction buffer 168 is dedicated to one of aplurality, e.g., four, symmetric multithreaded (SMT) hardware threads.An effective-to-real translation unit (iERAT) 172 is coupled to iCACHE170, and is used to translate instruction fetch requests from aplurality of thread fetch sequencers 174 into real addresses forretrieval of instructions from lower order memory. Each thread fetchsequencer 174 is dedicated to a particular hardware thread, and is usedto ensure that instructions to be executed by the associated thread isfetched into the iCACHE for dispatch to the appropriate execution unit.As also shown in FIG. 4, instructions fetched into instruction buffer168 may also be monitored by branch prediction logic 176, which provideshints to each thread fetch sequencer 174 to minimize instruction cachemisses resulting from branches in executing threads.

IU 162 also includes a dependency/issue logic block 178 dedicated toeach hardware thread, and configured to resolve dependencies and controlthe issue of instructions from instruction buffer 168 to XU 164. Inaddition, in the illustrated embodiment, separate dependency/issue logic180 is provided in AXU 166, thus enabling separate instructions to beconcurrently issued by different threads to XU 164 and AXU 166. In analternative embodiment, logic 180 may be disposed in IU 162, or may beomitted in its entirety, such that logic 178 issues instructions to AXU166. XU 164 is implemented as a fixed point execution unit, including aset of general purpose registers (GPR's) 182 coupled to fixed pointlogic 184, branch logic 186 and load/store logic 188. Load/store logic188 is coupled to an L1 data cache (dCACHE) 190, with effective to realtranslation provided by dERAT logic 192. XU 164 may be configured toimplement practically any instruction set, e.g., all or a portion of a32b or 64b PowerPC instruction set.

AXU 166 operates as an auxiliary execution unit including dedicateddependency/issue logic 180 along with one or more execution blocks 194.AXU 166 may include any number of execution blocks, and may implementpractically any type of execution unit, e.g., a floating point unit, orone or more specialized execution units such as encryption/decryptionunits, coprocessors, vector processing units, graphics processing units,XML processing units, etc. In the illustrated embodiment, AXU 166includes a high speed auxiliary interface to XU 164, e.g., to supportdirect moves between AXU architected state and XU architected state.

Communication with IP block 104 may be managed in the manner discussedabove in connection with FIG. 2, via network interface controller 108coupled to NOC 102. Address-based communication, e.g., to access L2cache memory, may be provided, along with message-based communication.For example, each IP block 104 may include a dedicated in box and/or outbox in order to handle inter-node communications between IP blocks.

Embodiments of the present invention may be implemented within thehardware and software environment described above in connection withFIGS. 1-4. However, it will be appreciated by one of ordinary skill inthe art having the benefit of the instant disclosure that the inventionmay be implemented in a multitude of different environments, and thatother modifications may be made to the aforementioned hardware andsoftware embodiment without departing from the spirit and scope of theinvention. As such, the invention is not limited to the particularhardware and software environment disclosed herein.

Software Pipelining

Turning now to FIG. 5, NOC 102 may be used in some embodiments toimplement a software-based pipeline. In particular, FIG. 5 illustratesan exemplary processing unit 200 incorporating a thread pipelinedsoftware engine 202 that may be used to implement and execute one ormore software pipelines 204 on top of an NOC architecture. Each pipeline204 is typically allocated one or more data structures 206 in a sharedmemory 208 to enable different stages of a pipeline to exchange data.Furthermore, an interrupt mechanism 210 is provided to enable stages ofa pipeline to notify one another of pending work to be performed.

One or more host interface processors (HIP's) 212 are also provided inengine 202 to handle the issue of work to software pipelines 204. One ormore push buffers 214 are provided to interface each HIP 212 with asoftware application 216 and driver 218, which are resident outside ofthe engine. In order to initiate work in a pipeline, a softwareapplication 216 issues requests through an appropriate driver 218 in theform of API calls, which then generates appropriate requests for the HIPand stores the requests in a push buffer 214. The HIP 212 for therelevant pipeline pulls work requests off of push buffer 214 andinitiates processing of the request by the associated pipeline.

In the illustrated embodiment, and as implemented on a NOC 102, asoftware pipeline 204 implements a function that is segmented into a setof modules or ‘stages’ of computer program instructions that cooperatewith one another to carry out a series of data processing tasks insequence. Each stage in a pipeline is composed of a flexiblyconfigurable module of computer program instructions identified by astage 1D with each stage executing on a thread of execution on an IPblock 104 of a NOC 102. The stages are flexibly configurable in thateach stage may support multiple instances of the stage, so that apipeline may be scaled by instantiating additional instances of a stageas needed depending on workload. Because each stage is implemented bycomputer program instructions executing on an IP block 104 of a NOC 102,each stage is capable of accessing addressed memory through a memorycommunications controller 106. At least one stage, moreover, is capableof sending network-address based communications among other stages,where the network-address based communications maintain packet order.

The network-address based communications, for example, may beimplemented using “inboxes” in each stage that receive data and/orcommands from preceding stages in the pipeline. The network-addressbased communications maintain packet order, and are communications of asame type which are able to flow through the same virtual channel asdescribed above. Each packet in such communications is routed by arouter 110 in the manner described above, entering and leaving a virtualchannel buffer in sequence, in FIFO order, thereby maintaining strictpacket order and preserving message integrity.

Each stage implements a producer/consumer relationship with a nextstage. The first stage receives work instructions and work piece datathrough a HIP 212, carries out its designated data processing tasks onthe work piece, produces output data, and sends the produced output datato the next stage in the pipeline, which consumes the produced outputdata from the first stage by carrying out its designated data processingtasks on the produced output data from the first stage, therebyproducing output data that is subsequently sent on to a next stage inthe pipeline. This sequence of operations continues to the last stage ofthe pipeline, which then stores its produced output data in an outputdata structure for eventual return through the HIP 212 to theoriginating application 216.

The arrangement of stages in a pipeline may vary in differentembodiments, as well as for performing different functions in differentapplications. FIG. 6, for example, illustrates an exemplary softwarepipeline 220 including a plurality of stage instances 222, alsoseparately designated as instances A-I, each of which representing athread of execution implemented on an IP block in NOC 102. The stageinstances 222 are arranged in pipeline 220 into five stages, a firststage with instance A, a second stage with instances B and C, a thirdstage with instances D, E and F, a fourth stage with instances G and H,and a fifth stage with instance 1. As can be seen from FIG. 6, instancesmay have a one-to-one, a one-to-many and/or a many-to-one relationshipwith other instances in the pipeline. Instances may operate collectivelywith one another in a particular stage to perform parallel tasks andshare the workload, thus improving the overall throughput of the stagein performing the task. Instances in a stage may also perform differenttasks from one another to enable the parallel performance of differenttasks. Instances can supply data to more than one instance, while otherinstances may collect data and process data from multiple instances.

In the illustrated embodiment, each instance of each stage of a pipelineis typically implemented as an application-level module of computerprogram instructions executed on a separate IP block on a NOC, and eachstage is assigned to a thread of execution on an IP block of a NOC. Eachstage is assigned a stage 1D, and each instance of a stage is assignedan identifier. HIP 212 (FIG. 5) typically sets up the pipeline byconfiguring each stage with a desired number of instances, with thenetwork location of each instance of each stage provided to otherinstances of other stages to enable each instance to send its resultantworkload to the proper instance in the next stage. Earlier and/or laterstage 3 to which an instance of stage 2 is authorized to send itsresultant workload. Multiple instances may be assigned to a particularstage to provide additional processing resources relative to otherstages, e.g., so work flows through the pipeline as efficiently aspossible, and no single stage presents a bottleneck to performance. Itwill also be appreciated that workload monitoring may be performedduring runtime, and that instances may be dynamically added or removedfrom a stage as needed for balancing the load among the stages of thepipeline.

Each stage is configured with a stage ID for each instance of a nextstage, which may also include the number of instances in the next stageas well as the network location of each instance of that. Configuring astage with IDs for instances of a next stage provides the stage with theinformation needed to carry out load balancing across stages. Such loadbalancing can be carried out, for example, by monitoring the performanceof the stages and instantiating a number of instances of each stage independence upon the performance of one or more of the stages. Monitoringthe performance of the stages can be carried out by configuring eachstage to report performance statistics to a separate monitoringapplication that in turn is installed and running on another thread ofexecution on an IP block or HIP. Performance statistics can include, forexample, time required to complete a data processing task, a number ofdata processing tasks completed within a particular time period, and soon, as will occur to those of skill in the art. Instantiating a numberof instances of each stage in dependence upon the performance of one ormore of the stages can be carried out by instantiating, by an HIP, a newinstance of a stage when monitored performance indicates a need for anew instance.

Hybrid Rendering Architecture

Now turning to FIG. 7, this figure illustrates an implementation ofprocessing unit 200 configured to implement a hybrid renderingarchitecture within which a dynamic ADS generator incorporating resetfunctionality consistent with the invention may be used. In particular,FIG. 7 illustrates a hybrid rendering software pipeline 230incorporating a streaming geometry frontend 232 interfaced with a raytracing backend 234 via a GIR generator 236. Streaming geometry frontend232 may be implemented, for example, as an OpenGL or DirectX compatiblefrontend, e.g., as is used in a number of different raster-basedtechniques, that streams a set of primitives for a scene. Frontend 232also may natively support the OpenGL or DirectX API's, and as such, maybe accessed by an application 216 developed for use with a raster-basedrendering algorithm via API calls that are converted by driver 218 intowork requests, which are sent to HIP 212 via push buffer 214 to initiateimplementation of those API calls by frontend 232.

GIR generator 236, in turn, processes the stream of primitives output bystreaming geometry frontend 232 to dynamically generate and store ageometry internal representation (GIR) data structure 238 in memory 208.GIR 238 functions as an accelerated data structure, and as such is usedby ray tracing backend 234 to render a frame of image data for a sceneto a frame buffer 240. GIR generator 236 dynamically generates the GIRusing a plurality of parallel threads of execution, and as such, reducesthe likelihood of GIR generation serving as a bottleneck on overallperformance. In addition, if desired, backend 234 is permitted to beginaccessing the GIR in parallel with the GIR generator dynamicallybuilding the GIR, and prior to the GIR generator completing the GIR. Asan alternative, backend 234 may not operate on the GIR until afterconstruction of the GIR is complete. As yet another alternative,frontend 232 and backend 234 may operate on different frames of data,such that frontend 232 streams primitive data to GIR generator 236 tobuild a GIR for one frame while backend 234 is processing the GIR for anearlier generated frame.

So configured, streaming frontend 232, GIR generator 236 and ray tracingbackend 234 are each amenable to execution by a plurality of parallelthreads of execution. Furthermore, GIR generator 236 serves to adapt theoutput of a streaming geometry frontend, ordinarily configured for usewith a raster-based backend, for use with a physical rendering backendsuch as a ray tracing or photon mapping backend. As such, the same APIas would be used for a raster-based rendering technique may berepurposed for physical rendering, often without requiring changes tothe API or to an application that makes calls to the API.

Dynamic ADS Generation

An ADS may be used to enable a physical rendering algorithm such as aray tracing algorithm to quickly and efficiently determine with whichregions of a scene an issued ray intersects any objects within a sceneto be rendered. An ADS may be implemented, for example, as a spatialindex, which divides a three-dimensional scene or world into smallervolumes (smaller relative to the entire three-dimensional scene) whichmay or may not contain primitives. An image processing system can thenuse the known boundaries of these smaller volumes to determine if a raymay intersect primitives contained within the smaller volumes. If a raydoes intersect a volume containing primitives, then a ray intersectiontest can be run using the trajectory of the ray against the knownlocation and dimensions of the primitives contained within that volume.If a ray does not intersect a particular volume then there is no need torun ray-primitive intersection tests against the primitives containedwithin that volume. Furthermore, if a ray intersects a bounding volumethat does not contain primitives then there is no need to runray-primitive intersections tests against that bounding volume. Thus, byreducing the number of ray-primitive intersection tests that may benecessary, the use of a spatial index greatly increases the performanceof a ray tracing image processing system. Some examples of differentspatial index acceleration data structures are oct-trees, kdimensionalTrees (kd-Trees), and binary space partitioning trees (BSP trees). Whileseveral different spatial index structures exist, and may be used inconnection with the physical rendering techniques disclosed herein, theillustrated embodiments rely on a branch tree implemented as a base btree split up into smaller trees of depth k.

By way of example, FIGS. 8 and 9 illustrate a relatively simple branchtree implementation that uses axis aligned bounding volumes to partitionthe entire scene or space into smaller volumes. That is, the branch treemay divide a three-dimensional space encompassed by a scene through theuse of splitting planes which are parallel to known axes. The splittingplanes partition a larger space into smaller bounding volumes. Togetherthe smaller bounding volumes make up the entire space in the scene. Thedetermination to partition (divide) a larger bounding volume into twosmaller bounding volumes may be made by the image processing systemthrough the use of a branch tree construction algorithm.

One criterion for determining when to partition a bounding volume intosmaller volumes may be the number of primitives contained within thebounding volume. That is, as long as a bounding volume contains moreprimitives than a predetermined threshold, the tree constructionalgorithm may continue to divide volumes by drawing more splittingplanes. Another criterion for determining when to partition a boundingvolume into smaller volumes may be the amount of space contained withinthe bounding volume. Furthermore, a decision to continue partitioningthe bounding volume may also be based on how many primitives may beintersected by the plane which creates the bounding volume.

The partitioning of the scene may be represented, for example, by abinary tree structure made up of nodes, branches and leaves. Eachinternal node within the tree may represent a relatively large boundingvolume, while the node may contain branches to sub-nodes which mayrepresent two relatively smaller partitioned volumes resulting after apartitioning of the relatively large bounding volume by a splittingplane. In an axis-aligned branch tree, each internal node may containonly two branches to other nodes. The internal node may contain branches(i.e., pointers) to one or two leaf nodes. A leaf node is a node whichis not further sub-divided into smaller volumes and contains pointers toprimitives. An internal node may also contain branches to other internalnodes which are further sub-divided. An internal node may also containthe information needed to determine along what axis the splitting planewas drawn and where along the axis the splitting plane was drawn.

FIG. 8, for example, illustrates an example two dimensional space to berendered by an image processing system, while FIG. 9 illustrates acorresponding branch tree 258, comprising nodes 260-268, for theprimitives shown in FIG. 8. For simplicity, a two dimensional scene isused to illustrate the building of a branch tree, however branch treesmay also be used to represent three-dimensional scenes. In the twodimensional illustration of FIG. 8, for example, splitting lines areillustrated instead of splitting planes, and bounding areas areillustrated instead of bounding volumes as would be used in athree-dimensional structure. However, one skilled in the art willquickly recognize that the concepts may easily be applied to athree-dimensional scene containing objects.

FIG. 8 illustrates a two dimensional scene 250 containing primitives252A, 252B and 252C to be rendered in the final image. The largestvolume which represents the entire volume of the scene is encompassed bybounding volume 1 (BV₁) (which is not shown separately in FIG. 8 becauseit encompasses the entire scene. In the corresponding branch tree thismay be represented by the top level node 260, also known as the root orworld node. In one embodiment, an image processing system may continueto partition bounding volumes into smaller bounding volumes when thebounding volume contains, for example, more than two primitives. Asnoted earlier the decision to continue partitioning a bounding volumeinto smaller bounding volumes may be based on many factors, however forease of explanation in this example the decision to continuepartitioning a bounding volume is based only on the number ofprimitives.

Thus, for example, as can be seen in FIG. 8, BV₁ may be broken into twosmaller bounding volumes BV₂ and BV₃ by drawing a splitting plane 254along the x-axis at point X₁. This partitioning of BV₁ is also reflectedin the branch tree as the two nodes 262 and 264, corresponding to BV₂and BV₃ respectively, under the internal (interior) or parent node BV₁260. The internal node representing BV₁ may now store information suchas, but not limited to, pointers to the two nodes beneath BV₁ (e.g., BV₂and BV₃), along which axis the splitting plane was drawn (e.g., x-axis),and where along the axis the splitting plane was drawn (e.g., at pointx₁).

Bounding volume BV₃ may then be broken into two smaller bounding valuesBV₄ and BV₅ by drawing a splitting plane 256 along the y-axis at pointY₁. Since BV₃ has been partitioned into two sub-nodes it may now bereferred to as an internal node. The partitioning of BV₃ is alsoreflected in the branch tree as the two leaf nodes 266 and 268,corresponding to BV₄ and BV₅, respectively. BV₄ and BV₅ are leaf nodesbecause the volumes they represent are not further divided into smallerbounding volumes. The two leaf nodes, BV₄ and BV₅, are located under theinternal node BV₃ which represents the bounding volume which waspartitioned in the branch tree.

The internal node representing BV₃ may store information such as, butnot limited to, pointers to the two leaf nodes (i.e., BV₄ and BV₅),along which axis the splitting plane was drawn (i.e., y-axis), and wherealong the axis the splitting plane was drawn (i.e., at point Y₁).

Thus, if a traced ray is projected through a point (X, Y) in boundingvolume BV₅, a ray tracing algorithm may quickly and efficientlydetermine what primitives need to be checked for intersection bytraversing through the tree starting at node 260, determining from the Xcoordinate of the point that the point is in bounding volume BV₃ andtraversing to node 264, determining from the Y coordinate of the pointthat the point is in bounding volume BV₅ and traversing to node 268.Node 268 provides access to the primitive data for primitives 252C, andthus, the ray tracing algorithm can perform intersection tests againstthose primitives.

FIGS. 10 and 11 next illustrate a branch tree generation algorithmsuitable for use in GIR generator 236 to generate a GIR implemented as aform of branch tree that is capable of being generated in a highlyparallel manner. The herein-described branch tree generation algorithmgenerates a dynamically built accelerated data structure (ADS) forstreaming data on a highly parallel machine, based upon a relativelybuilding and traversal algorithm, that uses minimal memory and memorybandwidth, and that typically requires no additional information thancommon rendering API's such as DirectX and OpenGL currently supply.

A branch tree generated by the herein-described embodiment isimplemented as a base b tree split up into smaller trees of depth k,where each small tree may be referred to as a branch. If a leaf node inthe branch is an interior node of the larger tree it will contain apointer to another branch continuing the tree. If objects are onlyallowed to be placed at leaf nodes of the smaller trees there is no needto contain the upper levels of the depth k tree and the tree cantherefore be looked at as a base bk tree. In one embodiment, the branchtree is an oct-tree split up into small trees of depth 2 that allowsdata to be stored only at even levels, which is essentially equivalentto a base 64 tree.

The branch tree may also be considered as an expanding grid. An initialgrid of 64 voxels is made. If small enough geometry exists inside one ofthese voxels, another 64 voxel grid, or branch, is made inside it. Thepattern is continued until a significant or maximum depth ofgrids/branches is reached. From the standpoint of storage, however, eachbranch is stored simply as 64 nodes, as shown below:

struct branch{  node nodes[64]; };

In the illustrated embodiment, the nodes of the branch are 4-byte wordsthat either contain a pointer to geometry, list of geometry, a nullvalue, or an indexed offset to another branch. If a node in the branchcontains one or more pieces of geometry it will contain a pointer to thegeometry or list of geometry. It is desirable for the address of thegeometry or geometry list to be larger than the number of branches thatwill make the tree as the node data type may be determined by the node'sunsigned integer value being larger or smaller than this threshold. If anode is empty it contains a null value. If it is an interior node itcontains an offset to the branch that continues the tree beyond it. Theoffset is an index into a list of branches that is built during theconstruction process of the tree. For example, a node may have astructure such as:

struct node{  union {   uint offset;   geometry *geo;   geometry_list *geo_list;  }; }while a geometry list may have a structure such as:

struct geometry_list{  uint num_geometry;  geometry * geo_ptr; };

In the illustrated embodiment, the construction of the branch tree isdesigned to be performed dynamically and in parallel. The algorithmrelies on two global variables, a pointer to the memory allocated forthe tree and an integer next_offset that stores an index into thismemory where a newly built branch can be stored. The index can either beshared globally or reserved memory can be split into groups to allowmultiple next_offset pointers to be used. For simplicity of description,a single next_offset will be assumed; however, multiple offsets may bedesirable in some embodiments to reduce memory conflicts.

The algorithm also is provided with the maximum depth allowed by thetree. Because float numbers have a 24 bit significand, it may bedesirable to enable each depth of a base 64 tree to use two bits in eachdirection, such that a maximum depth of max_d=12 may be used. A depthtwelve base 64 branch tree has the equivalent precision to a 64¹² voxelgrid.

To initialize the tree, the next_offset is set to 65 and a branch withall empty nodes (null value) is written to the first branch (top branch)in the memory allocation. No other steps are required.

Thereafter, each streamed geometry primitive from the streaming geometryfrontend is placed into the scene, using an instance of a routine suchas routine 270 of FIG. 10. Thus, the GIR generator is configured toexecute an instance of a placement routine in each of the plurality ofparallel threads of execution allocated to the GIR generator to insert aplurality of primitives into the branch tree in parallel.

The placement function receives as input a pointer to the geometry andthe three dimensional mins and maxs converted from float worldcoordinates to integer grid coordinates. The grid coordinates assume astep size of one at the maximum depth. In addition, by using a fewcompares instead of masks, the tree building process can typically beperformed without float to integer conversion.

Routine 270 begins in block 272 by deciding at which nodes to place thegeometry primitive. This process typically involves building keys fromthe min and max values. The keys can be built either with compares orfrom floats converted to integer values. In the illustrated embodiment,a compare with integer values is used. A 6 bit key is the node index inthe current branch and is built of a set of x, y and z integer valuesfor a point. The equation for building the tree is:

node_key[0:5]={x[2*(max_(—) d−d):+1], y[2*(max_(—) d−d):+1],z[2*(max_(—) d−d):+1]};

where d is the current depth of the branch and max_d is the maximumdepth of the tree where the nodes are cubes of integer volume 1.

The algorithm can find all nodes relating to the geometry primitive byfinding the x, y, and z components of the keys for the geometry's minand max points, and generating all possible keys between and includingthe min and max values. More precise methods may be used in thealternative.

Thus, block 274 initiates a FOR loop, and for each node, retrieves thenode in block 276, determines whether the node is an interior node inblock 278, and if not, jumps to the next branch in block 280.

If, however, a node is determined to be a leaf node, rather than aninterior node, block 278 passes control to block 282 to determinewhether to place the geometry primitive at the current depth in thetree. Two factors may be used to make this determination. The first iswhat type of node it is in. If the node is an interior node thengeometry exists below it and it will not be placed at that level, whichis determined in block 278. The second factor is the size of thegeometry primitive. In the illustrated embodiment, the geometryprimitive is placed if the node width is greater than four times themagnitude of the vector from the geometry primitive's min to max.

If the decision is made to place the geometry primitive, control passesto tag and add the geometry primitive in block 284, whereby theprimitive is placed and the current iteration of routine 270 iscomplete. If it is decided to not place the geometry primitive at thecurrent depth, the node is expanded in blocks 286, 288, 290 and 292.Specifically, block 288 recursively calls routine 270 to place thegeometry primitive in the new branch. Block 290 determines if any othergeometry exists in the node, and if so, passes control to block 292 torecursively place the other geometry in the node by calling routine 270for each tagged geometry primitive in the node. Upon completion of block292, or if the node is otherwise empty as determined in block 290,routine 270 is complete.

Thus, in the case of the node being an empty node, a new empty branch iscreated at the location indicated by *next_offset. The value of*next_offset is then stored in the expanding node and is incremented.This is how the tree is expanded and built. If the node containsexisting tagged geometry primitives, the geometry is buried in order toturn the current node into an interior node. The existing geometry isburied after placing the new geometry primitive as it is smaller andwill go deeper than the tagged geometry. As such, routine 270 ensuresthat all geometry gets pushed to the leaf nodes as they are expanded.Routine 270 therefore dynamically expands the branch tree whenever aprimitive needs to be inserted into a full branch.

FIG. 11 illustrates an add geometry routine 300 that may be called, forexample, in block 284 of routine 270 (FIG. 10). Routine 300 firstdetermines what state (empty, single geometry, geometry list) the nodeis in using blocks 302 and 304 and acts accordingly.

If the node's value is 0, the node is empty, and as such, block 302passes control to block 306 to link to the new geometry by replacing thevalue in the node with a pointer to the geometry primitive being placed,whereby routine 300 will be complete. If the node has a non-zero value,block 304 determines whether the node stores a pointer to a singlegeometry primitive or a list of geometry, by loading the value at thepointed to address as an unsigned integer. If this integer value isinclusively between one and the maximum number of primitives allowed(e.g., 15), the pointer is determined to be a geometry_list pointer, asthe value is the num_geometry component of a geometry_list. Otherwise,the value is considered to be a single geometry primitive.

It is important to note that float values or binary values equal tointeger values of 1 through 15 are permitted. In addition, by avoidingprocessing of a list when only a single geometry primitive exists in anode can save a significant amount of time and memory but is onlyapplicable if either only one type of geometry primitive exists in ascene or if the geometry primitive is provided with a type header.Otherwise some sort of list will be required for all primitives.

Geometry lists in the illustrated embodiment have an integernum_geometry indicating how many pieces of geometry are in the list, anda list of pointers to geometry. The allocated space for the number ofpointers is even to lower the number of reallocations necessary.Therefore when a new piece of geometry is added to the list, if thenum_geometry value is even, new memory space is allocated. If it is noteven, a pointer to the geometry is simply appended to the end of thepointer list. Num_geometry is incremented in both cases.

As such, if block 304 determines the node includes a single geometryprimitive, control passes to block 308 to make a geometry list and add alink for the new geometry primitive to the new list. Otherwise, block304 passes control to block 310 to determine if the list is full. Ifnot, block 312 adds the geometry primitive to the list. If the list isfull, block 314 determines if there are too many primitives in the node.If not, a new list is created with two additional spaces in block 316,and the new geometry primitive is linked into the list. If the node istoo full, however, block 318 buries the new and existing geometryprimitives by recursively calling routine 270.

Of note, routines 270 and 300 are capable of being used in a parallelhardware architecture, as multiple instantiations of such routines maybe used to concurrently place different primitives in the same branchtree. Consequently, assuming sufficient numbers of parallel threads ofexecution are allocated to an ADS generator that implements suchroutines, the generation of an ADS may occur at the same rate asprimitives are streamed from the streaming geometry frontend, and onceall of the primitive data has been streamed for a scene from thestreaming geometry frontend, a fully constructed ADS is almostimmediately available for use by a physical rendering backend.

Now turning to FIG. 12, as noted above, a number of streaming geometryfrontends may be used consistent with the invention. FIG. 12, forexample illustrates a raster-based streaming geometry frontend 330including a grouper 332, geometry engine 334 and post geometry enginemodule 336. Grouper 332 groups data for streaming down the pipeline,while geometry engine 334 performs object transformations and generatesthe geometry primitives. Module 336 performs operations such asperspective divides, culling, sorting, or breaking up geometry, and theend result output of module 336 is a stream of geometry primitives. Itwill be appreciated that a wide variety of streaming geometry frontendarchitectures may be used consistent with the invention, and as such,the invention is not limited to the particular architecture illustratedin FIG. 12.

FIG. 13 next illustrates a ray tracing implementation of a physicalrendering backend 340 consistent with the invention. Backend 340includes a master ray management module 342 that handles interfacingwith the rendering front end, initiating and synchronizing all initialrays, performing performance monitoring and dynamic (or static) loadbalancing. One or more other ray management modules 344 functions as aslave ray manager that receives rays from the master or other slaves andtraverses the ADS until determining if the ray intersects with a fullleaf node or not. If not, the default background color is applied. Ifso, the ray is sent to a ray primitive intersect module 346, whichdetermines the intersections between rays and primitives. A color updatemodule 348 updates pixels in a scene based upon the intersectionsdetected between rays and primitives. It will be appreciated that a widevariety of ray tracing backend architectures may be used consistent withthe invention, and as such, the invention is not limited to theparticular architecture illustrated in FIG. 13.

Implementation of a software pipeline to implement the aforementionedhybrid rendering functionality is illustrated at 400 in FIGS. 14A and14B. FIG. 14A, in particular primarily illustrates the frontend aspectsof the architecture, while FIG. 14B primarily illustrates the backendaspects of the architecture. Software pipeline 400 is implemented by aNOC resident in a graphics processor unit (GPU) coupled to a hostprocessor (CPU) via a bus, e.g., a PCI express bus 414.

As shown in FIG. 14A, an application 402 utilizes a driver 404 to submitwork requests to the software pipeline via a push buffer 406.Application 402 and driver 404 are executed on the CPU, while pushbuffer 406 is resident in shared memory accessible to both the CPU andthe GPU. Work requests are pulled from push buffer 406 by commandprocessing logic, and in particular a host interface processor (HIP)408. In addition, driver state information is maintained in allocatedmemory 410, 412 in the CPU and GPU, respectively. The states of the pushbuffer head and tail pointers for push buffer 406 are maintained at 416and 418 in memory 410 while the state of the tail pointer is maintainedat 420 in memory 420.

HIP 408 sets up the software pipeline, assigns threads of execution tostage instances in the pipeline, issues work requests to the pipeline,and monitors workflow to dynamically reallocate threads of execution todifferent stages of the pipeline to maximize throughput and minimizebottlenecks. In this regard, HIP 408, which is itself typicallyimplemented in an IP block from a NOC, assigns one or more IP blocks tohandle each stage of the pipeline, as well as other supporting logicthat may be required to manage operation of the pipeline. A thread ofexecution in this regard constitutes a hardware thread implementedwithin an IP block, it being understood that in IP blocks that supportmultiple hardware threads, multiple stage instances in a pipeline may beassigned to different threads in the same IP block.

Examples of supporting logic include DMA engines 422, 424, which arerespectively used to DMA vertex data from a vertex buffer 426 andcompressed texture data from a texture data buffer 428. A scratch memory430, including an index array 432, vertex buffer 434 and compressedtexture data 436, serves as a destination for DMA engines 422, 424. HIP408 sets up a set of inboxes 437 in DMA engines 422, 424 to receive workrequests from the HIP. One inbox 437 is provided for each DMA engineactivated in the pipeline.

An interrupt mechanism 441 is used in software pipeline 400 to enableinter-node communication between logical units in the pipeline. Nodes,e.g., HIP 408 and DMA engines 422, 424 receive interrupts from mechanism441, and are capable of issuing interrupts to other nodes via memorymapped input/output (MMIO) requests issued to the interrupt mechanism.

The frontend of pipeline 400 is implemented by a vertex processorincluding a first unit 450 configured as a grouper and a second unit 452configured as a geometry shader, and a texture processor 454.

HIP 408 initiates work in the vertex processor 450, 452 and textureprocessor 454 using inboxes 438, 440. At least one inbox 438 isallocated for each unit in the vertex processor, and at least one inbox440 is allocated for each unit in texture processor 454. In addition,HIP is capable of writing data to a render context table 442, vertexsort table 444, primitive sort table 446 and texture context table 48.Vertex processor unit 450 is responsive to requests fed to an inbox 438,and retrieves working data from index array 432 and vertex buffer 434.Unit 450 communicates with vertex processor unit 452 via an inbox 456and unit 452 outputs primitives to an array of inboxes 458, 460. Textureprocessor 454 receives requests from an inbox 440, reads texture data436 from scratch memory 430 and outputs to a texture memory 462.

As shown in FIG. 14B, a set of inboxes 458, 460 is allocated for each ofa plurality of GIR generator elements 464 that collectively implement aGIR generator, enabling the frontend of the pipeline to provideprimitive data for use in building a GIR 472. As noted above, aplurality of parallel threads of execution, e.g., one or more perelement 464, is used to generate the GIR in the manner described above.

One or more master ray management elements 466, one or more raymanagement elements 468, one or more ray primitive intersect elements470 and one or more color update elements 471 respectively implement aray tracing backend. A variable number of threads of execution may beallocated for each type of element 466, 468, 470, 471 in order tooptimize throughput through the software pipeline. Elements 466, 468 and470 use the GIR 472 to perform ray tracing operations, while elements470 retrieves texture data from texture memory 462. Communicationbetween stages of the backend is provided by inboxes 474, 476 and 478,respectively allocated to elements 468, 470 and 471. Color updateelements 471 output image data to a render target 480, e.g., an imagebuffer, which is then output via digital video out circuit 482.

It will be appreciated that the implementation of a streaming geometryfrontend and a ray tracing backend into the software pipeline elementsand underlying NOC architecture would be well within the abilities ofone of ordinary skill in the art having the benefit of the instantdisclosure. It will also be appreciated that different numbers ofelements may be used to implement each stage of the software pipeline,and that different stages may be used to implement the frontend and/orbackend of the pipeline based upon the particular algorithms usedthereby. Furthermore, by actively monitoring the workload of each stageof the pipeline, it may be desirable in some embodiments to dynamicallychange the allocation of IP blocks and threads of execution to differentstages of the pipeline, thus providing optimal throughput for differenttypes of tasks.

Reset of Dynamic ADS

As noted above, in the illustrated embodiments, an ADS is typicallyrebuilt for each image frame. Given that each ADS will typically have asizable memory allocation in working memory, it is often desirable forperformance reasons to avoid having to obtain such a sizable allocationfor each image frame by reusing the same allocation for each imageframe. To do so, the data in the memory allocation for the prior imageframe's ADS must be cleared out or otherwise reset so that the new ADScan be constructed. While the memory allocation could be cleared byoverwriting all previous data in the memory allocation, in manyinstances, doing so would take an extensive amount of time for themassive amount of data provided in a typical ADS.

Instead, consistent with the invention, a dynamic ADS stored in a memoryallocation may be reset by resetting only the beginning parameters ofthe new ADS to be “grown”. In an exemplary oct-tree ADS implementation,for example, the ADS may be reset by initializing its world or root nodeand any pointers it uses as it is built. Then as data is streamed intothe ADS, the tree is expanded and written as needed. At the expansion ofa node seven of the newly created nodes may then be wiped clean, howeverat this point there is a high probability of them being written towithin a few steps of the building algorithm, thus providing temporalcoherence and reducing memory access latency. Additionally they will belocated near the 8th node that is being written to, thus also providingspatial coherence. This method of growing the tree and simplyre-initializing the world node and building pointers thus saves thecostly task of flushing the entire allocation. It will also beappreciated that if the pipeline is extended such that multiple imageframes are being built simultaneously, the aforementioned algorithm maybe extended to multiple allocations.

In one embodiment consistent with the invention, an ADS may beimplemented as a base b tree split up into smaller trees of depth k,where each small tree may be referred to as a branch, and having asimilar structure to that described above in connection with FIGS.10-11, but with each node including two additional fields, a leafindicator field that designates a node as either a leaf or an interiornode, and an empty indicator field that designates whether the node isempty. For example, a node may have a structure such as:

struct node{  boolean leaf;  boolean empty;  union {   uint offset;  geometry *geo;   geometry_list * geo_list;  }; }

The leaf and empty fields may be represented by a single bit, or byother data structures, and the fields may alternatively be referred toby other nomenclature, e.g., to indicate whether a node is an interiornode or not, or to indicate whether a node is in use or not.

FIGS. 15 and 16 next illustrate routines that may be executed by dynamicGIR generator 236 of FIG. 7 to implement the reset functionalitydescribed herein. FIG. 15, in particular, illustrates a reset GIRroutine 500 that is executed by dynamic GIR generator 236 in order toinitialize a GIR (ADS) at the start of an image frame. Routine 500resets a prior GIR in a memory allocation (e.g., a GIR 238) simply byresetting the root node, in particular by setting the root node to aleaf node in block 502 and setting the root node to empty node in block504. Using the aforementioned branch tree data structure, routine 500 isthus capable of resetting the GIR merely by setting the two indicatorsin the node data structure discussed above, thus avoiding the need todeallocate the memory used for the GIR for the prior image frame, orclear out the data in the allocated memory.

Next, as shown in FIG. 16, primitives may be dynamically added to thereset GIR using an insert primitive routine 510. It will be appreciatedthat insert primitive routine 510 may be called to add each primitivestreamed from the front end of the software pipeline to the GIR, andthus, routine 510 may be executed on multiple hardware threads toparallelize the creation of the GIR.

Routine 510 begins in block 512 by determining whether the root node forthe GIR is a leaf node, e.g., by checking the leaf indicator in the nodedata structure for the root node. If so, indicating that no primitiveshave been added to the GIR since it was reset by routine 500, controlpasses to block 514 to set the root node to an interior node, e.g., byclearing the leaf indicator in the root node data structure, and markingthe node as non-empty. Next, block 516 sets all child nodes for the rootnode to leaf nodes and sets those child nodes to empty, by respectivelysetting the leaf and empty indicators for the child nodes. It will beappreciated that, while the child nodes are not created, generally thesenodes will still be resident in the allocated memory from the lastversion of the GIR, and may contain primitive data, or may haveoriginally been configured as interior nodes. By setting the leaf andempty indicators, however, these child nodes will be reset toinitialized states, as if the child nodes were newly created in thememory allocation. In some embodiments, it may be necessary or desirableto create the data structures for the child nodes (e.g., if this is thefirst GIR being generated by the GIR generator). In addition, while itmay be desirable to wipe out or overwrite the primitive data that may bestored in the child nodes, in the illustrated embodiment simply settingthe child nodes to “empty” effectively clears the prior primitive datafrom those nodes.

Block 516 then passes control to block 518 to select the child node inwhich to insert the primitive. In addition, returning to block 512, ifthe root node is not a leaf node, control likewise passes to block 518to select the child node in which to insert the primitive. Block 518operates by selecting to which among the children of the current node(initially the root node) the primitive belongs, in the general mannerdescribed above. Block 520 determines whether the selected node is aleaf node, and if not (indicating the node is an interior node), controlreturns to block 518 to select a child node of the newly selected node.In this manner, blocks 518 and 520 descend through the GIR to the firstencountered leaf node in the tree.

Once a leaf node is found, control passes to block 522 to determinewhether the selected node is full. If not, it is permissible to placethe primitive in the selected node, so control passes to block 524 toinsert the primitive in the selected node in the manner described above,and routine 510 is complete.

If, however, the selected node is full, block 522 passes control toblock 526 to set the selected node to an interior node (e.g., byclearing the leaf indicator for the node). Block 528 then sets all childnodes for the selected node to leaf nodes and to empty (e.g., by settingthe leaf and empty indicators for those nodes). As with the children ofthe root node, data structures for the children of the selected node mayneed to be created in the allocated memory, although in many instancesthe data structures from the GIR for the prior image frame will remainresident in the memory allocation.

Block 530 then pushes the primitives stored in the selected node down inthe GIR, e.g., by recursively calling routine 510 for each of thoseprimitives, as well as for the new primitive. Upon completion of block530, routine 510 is complete.

As a further illustration of the reset functionality described herein,FIGS. 17A-20B illustrate the reset and generation of a new ADS inallocated memory in a manner consistent with the invention. In thisimplementation, a quad tree is used for ease of understanding; however,it will be appreciated that the principles described herein will applyto any type of data structure suitable for implementing an ADS. As shownin FIG. 17A, a quad tree ADS 540 may include 13 nodes labeled A-M, withnode A being a root node 542, and with interior nodes 544 such as nodesB and H, and leaf nodes 546 such as nodes C-G and I-M.

FIG. 17B illustrates a memory allocation 550 within which ADS 540 mightbe stored. In this implementation, each node is represented by a nodedata structure that is either of fixed or variable size. A root nodedata structure 552 and interior node data structure 554 each definesempty and leaf fields (designated as “E” and “L”), along with linkfields (L1-L4) pointing to the respective child nodes. Thus, for rootnode A, link fields L1-L4 respectively point to nodes B, C, D and E, forinterior node B, link fields L1-L4 respectively point to nodes F, G, Hand I, and for interior node H, link fields L1-L4 respectively point tonodes J, K, L and M. In addition, for each root and interior node A, B,H the respectively leaf and empty indicators are cleared to indicatethat those nodes are neither leaf nodes nor are they empty.

A leaf node data structure 556 includes corresponding empty and leaffields, but instead of link fields, the leaf node data structureallocates space for the either the geometry primitives stored in theleaf node, or links to separate data structures in which the geometryprimitives are stored. In addition, each leaf node has its respectiveleaf indicator set. Furthermore, for the purposes of this example, it isassumed that each leaf node has at least one primitive stored therein,so the empty indicator for each such node is cleared.

While variable width node data structures may be used in someembodiments, in the embodiment shown in FIG. 17B, each node datastructure is of fixed size. By doing so, each link field may simplyprovide a relative or absolute offset to the referenced child node. Arelative offset, for example, may specify a value of “3” in the L4 fieldof root node data structure 552 to indicate that the link is to thethird node data structure after data structure 552, while an absoluteoffset would specify a value of “4” in the L4 field of root node datastructure 552 to indicate that the link is to the fourth node datastructure in memory allocation 550.

FIGS. 18A and 18B next illustrate the reset of the ADS stored in memoryallocation 550, e.g., after execution of a reset routine such as routine500 of FIG. 15, executed at the beginning of an image frame. As shown inFIG. 18B, root node data structure 552 is updated to set both the leafand empty indicators. By doing so, the link fields L1-L4 are effectivelyignored, and as shown in FIG. 18A, the new ADS 560 may be considered toinclude a single node, node A, functioning as a root node 562. Bysetting root node data structure 552 to a leaf node, the links to theother node data structures, for nodes B-M, are effectively broken;however, the data stored in these data structures need not be cleared orwiped out during the execution of routine 500. The nodes that are nolonger linked to the ADS are illustrated by cross-hatching in FIG. 18B.

FIGS. 19A and 19B next illustrate the condition of memory allocation 550during the first attempt to add a primitive to the new ADS, e.g., as aresult of execution of a routine such as routine 510 of FIG. 16. Asdescribed above in connection with FIG. 16, upon detecting that the rootnode is a leaf node in block 512, control passes to blocks 514-516 toset the root node to an interior node and to set all child nodes of theroot node to leaf nodes and empty nodes. Thus, as shown in FIG. 19B,root node data structure 552 is updated to clear the leaf and emptyindicators, and the data structures for each of the child nodestherefor, nodes B-E, are updated the set both the empty and leafindicators therefor. In addition, the links in the root node to thechild nodes are updated if necessary to point to nodes B-E. Of note, thedata in each child node may be cleared if desired (e.g., to remove thelinks from node B, as shown in FIG. 19B), or in the alternative, thedata may be retained until it becomes necessary to update the node datastructure. Thus, for example, if a primitive was being inserted in nodeB, the link data previously stored in that node may be initiallyretained after node B is set to be an empty leaf node, but overwrittenonce it was determined that the primitive needed to be stored in thatparticular node. It will be appreciated that upon completion of routine510 for a primitive, the primitive will typically be stored in one ofnodes B-D, and thus the respective empty indicator for that node will becleared in connection with storing the primitive data for the primitivein the node (not shown in FIG. 19B).

FIG. 19A illustrates the new ADS 560 after completion of blocks 514-516of FIG. 16, whereby node A, functioning as a root node 562, is onceagain linked to four nodes B-E, functioning as leaf nodes 564.

FIGS. 20A-20B next illustrate the state of ADS 560 after severalprimitives have been inserted into the ADS. In particular, as shown inFIG. 20A, after sufficient numbers of primitives have been inserted intothe ADS, the ADS will dynamically grow, e.g., to push down primitivedata to lower level nodes. Thus, node B is illustrated as beingconverted to an interior node, with its primitives pushed down to fourchild nodes F, G, H and I. The resulting memory allocation 550, shown inFIG. 20B, illustrates node B being converted to a non-empty interiornode, having cleared empty and leaf indicators, and with links L1-L4pointing to nodes F, G, H and I. Also, at this intermediate point, thenode data structures for nodes C, F and H are shown in non-empty states,such that at least one primitive is stored in each of those nodes.Nodes, D, E, G and I, however, remain empty by virtue of the set emptyindicators therefor.

In alternate embodiments of the invention, resetting an ADS may notresult in only the root node being retained in the ADS. For example, insome embodiments, it may be desirable to maintain one or more levels ofadditional nodes, and reset all of the lowest retained nodes to emptyleaf nodes, leaving any nodes above the leaf nodes in the hierarchy asinterior nodes. By doing so, at least a portion of the node structurefor the prior ADS may be retained. Given that in the majority ofsituations, successive image frames will be based upon the same basicscene, and thus the same primitives, often successive ADS's willultimately have a highly similar or identical structure. For example,resetting the ADS shown in FIG. 17A may result in an initial structurefor ADS 560 similar to that shown in FIGS. 19A and 19B, where a firstlevel of child nodes off of the root node are retained and initially setto empty leaf nodes.

Thus, embodiments of the invention are able to reset a dynamically grownADS in a manner that avoids deallocating and/or reallocating memory, aswell as clearing data in from a prior ADS stored in allocated memory. Inaddition, for kd-type trees, embodiments of the invention typicallyavoid the need to reset all boundaries in connection with resetting theADS.

Various modifications may be made without departing from the spirit andscope of the invention. For example, interior nodes in someimplementations may not require separate empty indicators, as interiornodes may be presumed to be empty from the standpoint of storingprimitive data. In addition, the data structures used for interior andleaf nodes need not be the same size and/or configuration. Othermodifications will be apparent to one of ordinary skill in the art.Therefore, the invention lies in the claims hereinafter appended.

1. A method of building accelerated data structures for use in imageprocessing, the method comprising: generating a first accelerated datastructure for a first image frame, the first accelerated data structureincluding a plurality of linked nodes for which memory is allocated in aworking memory, the plurality of linked nodes including a root node; andgenerating a second accelerated data structure for a second image frame,wherein generating the second accelerated data structure includes:initializing the second accelerated data structure by reusing the rootnode for the first accelerated data structure as a root node for thesecond accelerated data structure and resetting at least one node in thefirst accelerated data structure to break a link between the reset nodeand a linked-to node among the plurality of nodes; and dynamicallyadding a plurality of primitives from a scene to the second accelerateddata structure for the second image frame, including dynamicallyexpanding the reset node by reestablishing the link between the resetnode and the linked-to node and clearing primitive data from the firstaccelerated data structure that is stored in the linked-to node.
 2. Themethod of claim 1, wherein resetting the reset node includes resettingthe root node.
 3. The method of claim 1, wherein the plurality of nodesincludes a plurality of interior nodes linked to the root node and aplurality of leaf nodes linked to the plurality of interior nodes,wherein resetting the reset node includes resetting a first portion ofthe plurality of interior nodes without resetting the root node and asecond portion of the plurality of interior nodes.
 4. The method ofclaim 1, wherein initializing the second accelerated data structure isperformed without deallocating memory allocated to the linked-to node.5. The method of claim 1, wherein initializing the second accelerateddata structure is performed without clearing primitive data from thefirst accelerated data structure that is stored in the linked-to node.6. The method of claim 1, wherein dynamically expanding the reset nodeincludes changing the reset node from a leaf node to an interior node.7. The method of claim 6, wherein changing the reset node from a leafnode to an interior node includes updating an indicator stored in thereset node to indicate that the reset node is an interior node.
 8. Themethod of claim 6, wherein dynamically expanding the reset node includespushing primitive data stored in the reset node to the linked-to node.9. The method of claim 1, wherein clearing primitive data from the firstaccelerated data structure that is stored in the linked-to node includesupdating an empty indicator stored in the linked-to node to indicatethat the linked-to node is empty.
 10. The method of claim 1, whereingenerating the first and second accelerated data structures areperformed by a software pipeline implemented on an interconnected set ofhardware-based processing elements, each including at least one separatehardware thread.
 11. The method of claim 10, wherein the interconnectedset of hardware-based processing elements comprises a plurality of nodesinterconnected to one another in a Network On Chip (NOC) arrangement.12. The method of claim 1, wherein the accelerated data structurecomprises a base b branch tree split up into a plurality of branches ofdepth k, wherein generating the second accelerated data structureincludes dynamically expanding the branch tree whenever a primitiveneeds to be inserted into a full branch.
 13. A circuit arrangement,comprising: at least one processing element; and program code executedby the at least one processing element and configured to buildaccelerated data structures in a working memory by generating a firstaccelerated data structure for a first image frame and generating asecond accelerated data structure for a second image frame, wherein thefirst accelerated data structure includes a plurality of linked nodesfor which memory is allocated in a working memory, wherein the pluralityof linked nodes includes a root node, wherein the program code isconfigured to generate the second accelerated data structure byinitializing the second accelerated data structure by reusing the rootnode for the first accelerated data structure as a root node for thesecond accelerated data structure and resetting at least one node in thefirst accelerated data structure to break a link between the reset nodeand a linked-to node among the plurality of nodes, and wherein theprogram code is further configured to generate the second accelerateddata structure by dynamically adding a plurality of primitives from ascene to the second accelerated data structure for the second imageframe, including dynamically expanding the reset node by reestablishingthe link between the reset node and the linked-to node and clearingprimitive data from the first accelerated data structure that is storedin the linked-to node.
 14. The circuit arrangement of claim 13, whereinthe program code is configured to reset the reset node by resetting theroot node.
 15. The circuit arrangement of claim 13, wherein theplurality of nodes includes a plurality of interior nodes linked to theroot node and a plurality of leaf nodes linked to the plurality ofinterior nodes, wherein the program code is configured to reset thereset node by resetting a first portion of the plurality of interiornodes without resetting the root node and a second portion of theplurality of interior nodes.
 16. The circuit arrangement of claim 13,wherein the program code is configured to initialize the secondaccelerated data structure without deallocating memory allocated to thelinked-to node.
 17. The circuit arrangement of claim 13, wherein theprogram code is configured to initialize the second accelerated datastructure without clearing primitive data from the first accelerateddata structure that is stored in the linked-to node.
 18. The circuitarrangement of claim 13, wherein the program code is configured todynamically expand the reset node by changing the reset node from a leafnode to an interior node.
 19. The circuit arrangement of claim 18,wherein the program code is configured to change the reset node from aleaf node to an interior node by updating an indicator stored in thereset node to indicate that the reset node is an interior node.
 20. Thecircuit arrangement of claim 18, wherein the program code is configuredto dynamically expand the reset node by pushing primitive data stored inthe reset node to the linked-to node.
 21. The circuit arrangement ofclaim 13, wherein the program code is configured to clear primitive datafrom the first accelerated data structure that is stored in thelinked-to node by updating an empty indicator stored in the linked-tonode to indicate that the linked-to node is empty.
 22. The circuitarrangement of claim 13, wherein the program code is executed by asoftware pipeline implemented on an interconnected set of hardware-basedprocessing elements, each including at least one separate hardwarethread.
 23. The circuit arrangement of claim 13, wherein the accelerateddata structure comprises a base b branch tree split up into a pluralityof branches of depth k, wherein generating the second accelerated datastructure includes dynamically expanding the branch tree whenever aprimitive needs to be inserted into a full branch.
 24. An integratedcircuit device including the circuit arrangement of claim
 13. 25. Aprogram product, comprising: a computer readable medium; and programcode stored on the computer readable medium and configured to buildaccelerated data structures in a working memory by generating a firstaccelerated data structure for a first image frame and generating asecond accelerated data structure for a second image frame, wherein thefirst accelerated data structure includes a plurality of linked nodesfor which memory is allocated in a working memory, wherein the pluralityof linked nodes includes a root node, wherein the program code isconfigured to generate the second accelerated data structure byinitializing the second accelerated data structure by reusing the rootnode for the first accelerated data structure as a root node for thesecond accelerated data structure and resetting at least one node in thefirst accelerated data structure to break a link between the reset nodeand a linked-to node among the plurality of nodes, and wherein theprogram code is further configured to generate the second accelerateddata structure by dynamically adding a plurality of primitives from ascene to the second accelerated data structure for the second imageframe, including dynamically expanding the reset node by reestablishingthe link between the reset node and the linked-to node and clearingprimitive data from the first accelerated data structure that is storedin the linked-to node.